Search Results

You are looking at 1 - 3 of 3 items for

  • Author or Editor: Sarah Monette x
  • All content x
Clear All Modify Search
Sarah A. Monette and Justin M. Sieglaff

Abstract

The probability of turbulence in the region of identified cloud-top cooling (CTC) from a satellite-based algorithm is calculated. It is found that the overall turbulence probability is low, with only 3.93% of 738 Boeing 737s and 757s experiencing light or greater turbulence. Predicting the probability of turbulence is done using a Bayesian scheme. This prediction scheme relies on the CTC magnitude as well as the relationship between the CTC and aircraft. At higher CTC magnitudes [≤−16 K (15 min)−1], turbulence is more common, with the conditional probability of turbulence exceeding the conditional probability of no turbulence. Aircraft with flight levels that are less than 8000 ft (~2440 m) above the cloud height also have a higher conditional probability of turbulence than no turbulence. Overall, the Bayesian scheme is found to be more skillful when compared with use of climatological information alone.

Full access
Sarah A. Monette, Christopher S. Velden, Kyle S. Griffin, and Christopher M. Rozoff

Abstract

A geostationary satellite–derived cloud product that is based on a tropical-overshooting-top (TOT) detection algorithm is described for applications over tropical oceans. TOTs are identified using a modified version of a midlatitude overshooting-top detection algorithm developed for severe-weather applications. The algorithm is applied to identify TOT activity associated with Atlantic Ocean tropical cyclones (TCs). The detected TOTs can serve as a proxy for “hot towers,” which represent intense convection with possible links to TC rapid intensification (RI). The purpose of this study is to describe the adaptation of the midlatitude overshooting-top detection algorithm to the tropics and to provide an initial exploration of possible correlations between TOT trends in developing TCs and subsequent RI. This is followed by a cursory examination of the TOT parameter’s potential as a predictor of RI both on its own and in multiparameter RI forecast schemes. RI forecast skill potential is investigated by examining empirical thresholds of TOT activity and trends within prescribed radii of a large sample of developing North Atlantic TC centers. An independent test on Atlantic TCs in 2006–07 reveals that an empirically based TOT scheme has potential as a predictor for RI occurring in the subsequent 24 h, especially for RI maximum wind thresholds of 25 and 30 kt (24 h)−1 (1 kt ≈ 0.5 m s−1). As expected, the stand-alone TOT-based RI scheme is comparatively less accurate than existing objective multiparameter RI prediction methods. A preliminary experiment that adds TOT-based predictors to an objective logistic regression-based scheme is shown to improve slightly the forecast skill of RI, however.

Full access
Clark Evans, Heather M. Archambault, Jason M. Cordeira, Cody Fritz, Thomas J. Galarneau Jr., Saska Gjorgjievska, Kyle S. Griffin, Alexandria Johnson, William A. Komaromi, Sarah Monette, Paytsar Muradyan, Brian Murphy, Michael Riemer, John Sears, Daniel Stern, Brian Tang, and Segayle Thompson

The Pre-Depression Investigation of Cloud-systems in the Tropics (PREDICT) field experiment successfully gathered data from four developing and four decaying/nondeveloping tropical disturbances over the tropical North Atlantic basin between 15 August and 30 September 2010. The invaluable roles played by early career scientists (ECSs) throughout the campaign helped make possible the successful execution of the field program's mission to investigate tropical cyclone formation. ECSs provided critical meteorological information— often obtained from novel ECS-created products—during daily weather briefings that were used by the principal investigators in making mission planning decisions. Once a Gulfstream V (G-V) flight mission was underway, ECSs provided nowcasting support, relaying information that helped the mission scientists to steer clear of potential areas of turbulence aloft. Data from these missions, including dropsonde and GPS water vapor profiler data, were continually obtained, processed, and quality-controlled by ECSs. The dropsonde data provided National Hurricane Center forecasters and PREDICT mission scientists with real-time information regarding the characteristics of tropical disturbances. These data and others will serve as the basis for multiple ECS-led research topics over the years to come and are expected to provide new insights into the tropical cyclone formation process. PREDICT also provided invaluable educational and professional development experiences for ECSs, including the opportunity to critically evaluate observational evidence for tropical cyclone development theories and networking opportunities with their peers and established scientists in the field.

Full access